Calling Bullshit: The Art of Skepticism in a Data-Driven World
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- Author：Carl T. Bergstrom
- ISBN：0525509208
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Summary
Bullshit isn't what it used to be。 Now， two science professors give us the tools to dismantle misinformation and think clearly in a world of fake news and bad data。
"A modern classic 。 。 。 a straight-talking survival guide to the mean streets of a dying democracy and a global pandemic。"--Wired
Misinformation， disinformation， and fake news abound and it's increasingly difficult to know what's true。 Our media environment has become hyperpartisan。 Science is conducted by press release。 Startup culture elevates bullshit to high art。 We are fairly well equipped to spot the sort of old-school bullshit that is based in fancy rhetoric and weasel words， but most of us don't feel qualified to challenge the avalanche of new-school bullshit presented in the language of math， science， or statistics。 In Calling Bullshit， Professors Carl Bergstrom and Jevin West give us a set of powerful tools to cut through the most intimidating data。
You don't need a lot of technical expertise to call out problems with data。 Are the numbers or results too good or too dramatic to be true？ Is the claim comparing like with like？ Is it confirming your personal bias？ Drawing on a deep well of expertise in statistics and computational biology， Bergstrom and West exuberantly unpack examples of selection bias and muddled data visualization， distinguish between correlation and causation， and examine the susceptibility of science to modern bullshit。
We have always needed people who call bullshit when necessary， whether within a circle of friends， a community of scholars， or the citizenry of a nation。 Now that bullshit has evolved， we need to relearn the art of skepticism。
"A modern classic 。 。 。 a straight-talking survival guide to the mean streets of a dying democracy and a global pandemic。"--Wired
Misinformation， disinformation， and fake news abound and it's increasingly difficult to know what's true。 Our media environment has become hyperpartisan。 Science is conducted by press release。 Startup culture elevates bullshit to high art。 We are fairly well equipped to spot the sort of old-school bullshit that is based in fancy rhetoric and weasel words， but most of us don't feel qualified to challenge the avalanche of new-school bullshit presented in the language of math， science， or statistics。 In Calling Bullshit， Professors Carl Bergstrom and Jevin West give us a set of powerful tools to cut through the most intimidating data。
You don't need a lot of technical expertise to call out problems with data。 Are the numbers or results too good or too dramatic to be true？ Is the claim comparing like with like？ Is it confirming your personal bias？ Drawing on a deep well of expertise in statistics and computational biology， Bergstrom and West exuberantly unpack examples of selection bias and muddled data visualization， distinguish between correlation and causation， and examine the susceptibility of science to modern bullshit。
We have always needed people who call bullshit when necessary， whether within a circle of friends， a community of scholars， or the citizenry of a nation。 Now that bullshit has evolved， we need to relearn the art of skepticism。
Reviews
Connor
，
This book was so much fun。 It has that special sauce。 It made me feel the way I felt when I first read “Freakonomics” or those early Malcolm Gladwell books: the authors take you on a ride through their field of expertise， and you are going to be surprised and delighted。 For there truly is far too much bullshit in the world， and until now there has been no winsome strategy for how to call it out。 But Bergstrom and West aren’t stodgy referees。 Instead， they’re like your older brother’s college fri This book was so much fun。 It has that special sauce。 It made me feel the way I felt when I first read “Freakonomics” or those early Malcolm Gladwell books: the authors take you on a ride through their field of expertise， and you are going to be surprised and delighted。 For there truly is far too much bullshit in the world， and until now there has been no winsome strategy for how to call it out。 But Bergstrom and West aren’t stodgy referees。 Instead， they’re like your older brother’s college friends who， for some reason， are totally cool with helping you on your homework。 Most of all， I left this book feeling less overwhelmed with misinformation and how to respond to it。 。。。more
Jonas Goessaert
，
Af en toe redelijk droge materie， maar in het vervolg filter ik de bullshit er wel uit ;-）
Nikki
，
I enjoyed this。 The authors go into various ways things can be skewed and thus become bullshit instead of legit information。 They also point out that often this is not done maliciously。 We see how headlines can be less than helpful， like when they end up misrepresenting data or a study。 They go into how to look at data visualizations with a critical eye。 Social media and sharing is touched upon several times， sometimes with more focus and depth than other times。 They've also got a whole section I enjoyed this。 The authors go into various ways things can be skewed and thus become bullshit instead of legit information。 They also point out that often this is not done maliciously。 We see how headlines can be less than helpful， like when they end up misrepresenting data or a study。 They go into how to look at data visualizations with a critical eye。 Social media and sharing is touched upon several times， sometimes with more focus and depth than other times。 They've also got a whole section of the last chapter dedicated to instructing people to not be a "Well， actually。。。" guy， including a footnote about the deliberateness in using a gendered term （guy） in the description。 Overall， this book is informative， helpful， timely， important for everyone not just data nerds。 And it's funny。 Honestly， there's some great humor in here。 。。。more
Andrzej
，
Super important book to read nowadays。 For everyone。 Shows just how we as a society treat science and technology as a contemporary magic。 This book reminds us of the basic principles of scientific method and how it helps to interpret information and gives tools to criticize unsound research。
Zach
，
This book says it is adopted from a college course and I think that fact is one of the book's most prominent characteristics。 It's not a great field guide。 It's not a tightly written pop science book。 It's a series of loosely connected lectures， for an audience of already-educated people， in text form， with a wrapup at the end so you can reflect before the exam。 I find the structure really painful because it's a topic that badly needs a readable， digestible， entertaining book you can easily reco This book says it is adopted from a college course and I think that fact is one of the book's most prominent characteristics。 It's not a great field guide。 It's not a tightly written pop science book。 It's a series of loosely connected lectures， for an audience of already-educated people， in text form， with a wrapup at the end so you can reflect before the exam。 I find the structure really painful because it's a topic that badly needs a readable， digestible， entertaining book you can easily recommend to your friends and， uh， people who need to read something like this。 Instead， this book is a dense text packed with jargon that wants you to work your way through some difficult and counterintuitive concepts on your own as it leaps from topic to topic。 I guess you could pick up this book and skip to the last two chapters， but even those chapters are just summaries and don't have what I'm really looking for。 I would not recommend this book without a lot of caveats。The authors claim they don't want to be partisan， and so they are choosing a scattershot of illustrative stories across multiple disciplines。 It really is scattered - from personal anecdotes about Colorado skiing to thought experiments involving grouse hunting to machine learning papers about phrenology to 3d graphs about manure production to Hillary Clinton's instagram to Hillary Clinton's instagram again。 That's weird。 Then a graph about Stand Your Ground laws。 Well， so much for avoiding partisanship。 I found this scattershot approach incredibly distracting。 Lots of time is spent explaining context for crime rates in suburbs vs inner city core， then lighting conditions for photos in resume headshots versus in prison lineups， and several other unrelated fields。 If they stuck to one topic across the entire book （or even per chapter！）， gave the context up front， and then showed how to bullshit it according to the methods they wanted to show， we could have saved a lot of time and space。 Scientists get a chapter of sympathetic examination looking at the various pressures that cause the the appearance of contradictory results， but it sometimes feels like everything else from Jersey Shore （？？？） to social media to newspaper headlines gets one-sentence potshots about their evil influence on society。 Ironically， these prejudices don't always pass the author's own filters for detecting and calling bullshit。 Hey， if headlines are saying Russian trolls caused unprecedented division in American politics by making Facebook posts， doesn't that sound a bit bad to be true？ Shouldn't we examine that？ Too late， we're galloping off to the next topic。 It would be one thing if every single example used was a shocking example of bullshit， but some of the concepts the authors discuss honest come across as nitpicking instead of enlightening。 I think pointing out that framing imprecise metaphors in the framework of very precise things like mathematical formulas is fair game， but the amount of ink spilled on the misuse of periodic tables seems like overkill。 Sure， a "periodic table of music" is kind of bullshit in an abstract way， but does it matter？ Does it really cause harm， or is just a clumsy marketing gimmick？ The authors seem too upset at the corruption of a useful scientific concept to say。 Similarly， the first shot at Hillary Clinton's Instagram complains a bar is "labeled 75 percent but stretches 78 percent of the way to the right edge"。 Compelling stuff。 Oh， sorry， I truncated in a way that removes it from context - a clear sign of bullshit！ Here's the next sentence: "The bar for Asian women is even more misleading。 It is labeled 84 percent but extends a full 90 percent of the way to the right edge"。 Chilling！I'm a technical guy。 I work in software， I generate graphs and data to explain complicated concepts to less technical people， and I consider myself a pretty good skeptic。 I found a lot of the technical and mathematical details went over my head a bit。 I felt like in order to understand things I needed to stop reading， sit， think， reread， think， reread， and think some more。 That's fair for a college course。 That's fair for most books。 Unfortunately I don't think that's fair for a book that is supposed to address an urgent need， or a book that promises to teach a skill that it claims is badly needed。 I really appreciate it when an author is able to take the time to approach problems from multiple angles， and puts effort into making something understandable。 That's the difference between a book technically covering a material， and being an excellent reference you can recommend to other people。 I do not feel this book met that bar。 The really killer part is that I wanted to love this book。 I think the topic is incredibly important。 I follow Carl Bergstrom on Twitter， which is how I found out about the book， and he seems like a delightful guy。 The last two chapters come so close to what I wanted - a field guide of useful techniques to keep in mind and use。 On their own， they are too thin。 And with the structure of the book as it is， they are too frustrating to put into practice。 I don't feel it taught me a new skillset。 I do think I picked up a few fun anecdotes and a useful technique。 。。。more
Raghu
，
There was a time when we trusted a news item if it appeared in one of the reputed media outlets。 Newspapers like New York Times， Washington Post， and newsreaders like Walter Cronkite had the credibility and trust of the public。 But things are not that simple anymore。 Lies， disinformation， and misinformation have been around ever since humankind invented the printing press。 We have added fake news， fake images， and fake videos to this collection in the era of the Internet。 Sharp political polariz There was a time when we trusted a news item if it appeared in one of the reputed media outlets。 Newspapers like New York Times， Washington Post， and newsreaders like Walter Cronkite had the credibility and trust of the public。 But things are not that simple anymore。 Lies， disinformation， and misinformation have been around ever since humankind invented the printing press。 We have added fake news， fake images， and fake videos to this collection in the era of the Internet。 Sharp political polarization has made most media hyper-partisan。 Even scientific literature has become suspect because of quite a few papers of dubious quality passing the vetting of peer review in reputed journals。 We come across fancy graphs， math equations， and statistics that are downright false or half-truths。 In such an atmosphere， it is hard to filter truth from lies。 We need professional help， and that is what this brilliant book provides。 It educates by asking us to cultivate and keep a healthy skepticism。 It provides us with fundamental tools we can use in detecting and refuting lies， falsehoods， and fake news。 The authors use the term ‘Bulls**t’ to refer to them。 It is also in the book’s title。 （I shall use the abbreviation ‘BS’ instead of ‘Bulls**t’ from hereon so that it will pass muster with the standards of Goodreads and Amazon）。The book comprises four modules of discussion。 The first eight chapters in the book deal with the means through which the modern internet era presents BS to us。 Then comes a chapter on the positives and susceptibilities of science as a method。 The next two chapters discuss how to identify BS， and how to call and refute it。 The techniques for presenting BS as if they are the truth employ many methods。 Deploying spurious correlations or confusing us to mistake correlation for causation are time-worn techniques。 Numbers are another vehicle。 While words may seem verbose and manipulative， numbers have the inherent feel of not being subjective。 They give a scientific feel and suggest precision， having an independent existence in Nature。 Selection bias is yet another way to present BS。 When we select individuals， groups， or data for analysis in a way that we compromise randomization， we engage in selection bias。 Data visualization examples present BS in another form。 Programs like Excel and Google spreadsheets make it simple for anyone to generate bar charts， pie charts， graphs， and fancy-looking tables and images。 These entities tell stories。 Subtle choices， such as the range of the axes in a bar chart or line graph， can affect the story a figure tells。 They can reflect the underlying data with rigor or tell a story the designer wants you to believe。 Big Data and Artificial Intelligence are brilliant innovations。 But they are also the most recent methods misused to present BS。 Choice of the database， assumptions， and biases in the algorithms and data are some contributors。 Inaccessibility of the reasoning in a neural network is another。 Now that we know how BS gets presented， we apply them to spotting BS。 The authors give a checklist of actions to help us。 Whether we scan our social media feeds， listen to the news， or read an essay or article on improving our health， we must apply the following list of validations。1。 Question the source of the information。 Information without access to its source is suspect。 Ask who is saying this。 How does the person know it？ What is this person trying to sell me？2。 Beware of unfair comparisons。 For example， we see lists of the most violent cities in the US， ranked 1 to 10， in the media。 Often， they are meaningless because the entities used may not be comparable。 The book discusses one such example。3。 Remember the dictum， ‘If it sounds too good or too bad to be true， then it probably is’。 The media posts many claims to attract attention。 Some are often too extreme to be true。 Search the web for other sources， making the same claim。 The book illustrates it with an example from an NBC tweet on US universities。4。 Think in orders of magnitudes。 For example， the National Geographic Society warned that we dump nine billion tonnes of plastic waste into the oceans every year。 The authors show how we can question it in terms of magnitudes。 There are only seven to eight billion people on Earth。 So， it means each one of us dumps one tonne of plastic waste every year in the ocean。 Does it sound credible？5。 Avoid confirmation bias。 This is a well-known principle and does not need elaboration。6。 Consider multiple hypotheses。 In May 2018， Reuters reported that Walt Disney shares dropped 2。5% after ABC TV canceled the ‘Roseanne’ show for racism。 Disney’s revenue for 2017 was $55 billion。 The Roseanne show generated $45 million in 2018。 Does it sound plausible when a show that generated 0。1% of Disney’s revenue causes its shares to drop 2。5%？ The stock market had a dramatic drop that day and Disney too dropped 2。5%， well before the ABC announcement。7。 Misinformation appears as images， videos， or text。 Use tools on the web like reverse image lookup or fact-checking websites to find the truth。 When you use social media， remember the mantra， ‘think more， share less’。After spotting BS， it is not enough to just identify it。 We must call it out and refute it。 The authors show the methods we can adopt to refute BS in the last section of the book。1） A lot of fake news gets posted on the web with dubious numbers and statistics， to bolster its credibility。 We can use the ‘reductio ad absurdum’ principle to expose them。 In the summer Olympics of 2004， Yuliya Nesterenko ran the 100-meters in 10。93 seconds。 It was two seconds faster than what women did seventy years earlier。 Inspired by this， researchers saw that the gap between men’s and women’s timings is shrinking all the time。 So， they predicted that in the year 2156， women will outpace men， based on a rather simplistic model。 Others called it out by using the same model to show that women would run 100 meters in less than zero seconds by the year 2636！2） Look for counterexamples to refute doubtful claims。 One researcher in the Santa Fe Institute， New Mexico， claimed: ‘In order to survive in a pathogen-filled environment like ours， long-lived multicellular organisms such as the humans must have certain distinctive features in the immune system’。 Though it seemed a reasonable argument， an experienced immunologist posed the counter-question， ‘what about trees？’。 Trees are also long-lived multicellular organisms。 They have immune systems but with few of the features claimed by the researcher。3） Use analogies to help re-contextualize claims that may seem reasonable at first glance。 By drawing parallels between an unfamiliar situation and an example we understand with ease， we gain confidence in our abilities of critical thinking。4） Redrawing figures， graphs， and bar charts can help to spot BS。 Using the ‘Null model’ helps to refute misinformation in other instances。 Throughout the book， the authors make a powerful plea to place our trust in Science and keep a skeptical outlook。 According to them， science is the best method we have to cut through BS and seek Truth。 Science is self-correcting。 Every claim is open to challenge， and evidence can falsify every model or fact。 Science is a cumulative process。 It progresses when researchers build upon previous results。 If a result is false， we cannot build upon it and our efforts would fail。 This will make us go back and reassess the original findings。 This way， the truth will come out。 As science moves toward accepting some claim as a fact， experiments that contradict that claim become noteworthy。 It treats them as if they are positive results。 Science allows us to understand the nature of the physical world at scales far beyond the capabilities of our senses and the evolution of our minds。 We have created technologies that would seem magical to those a few generations ago。 However， science is not an absolute guarantor of truth。 Many scientific theories and results have been wrong in history and will continue to be so。 But science is empirically successful。 Despite its failures， the institution of science is strong。 It is important to keep this perspective when evaluating human knowledge - and BS - that is out there。The book results from a popular course of the same title as the book。 The authors taught the course to students at the University of Washington。 I found the book playful in tone and also serious。 An extensive number of examples illustrate every idea the authors advance。 It makes the book an engaging and purposeful read。 Many of us would already have come across several of the principles expressed here。 But the authors string them together in a composite story that is a masterclass。 Given the complexities of the times we live in， this book becomes very important for us to read and teach both at the high school and university levels。 If at all I have one criticism of the book， it is with the examples chosen。 The authors give examples of BS only from the right-wing of the political spectrum。 There are hardly any examples from the Left and Liberal spectrum。 History tells us that the entire political spectrum has disseminated BS to advance its agenda。 For decades， the Left tried to paint Soviet communism as more benign than it was using the techniques given in this book。 Environmentalists have presented distortions of the truth to promote their agendas。 It is important to stress that we need to be skeptical of the entire political spectrum and just not the Right。 All of them have their vested interests to advance through falsehoods， couched in the language of Maths and science。 This is a necessary book of our times。 。。。more
Saadia
，
Love the actual walkthroughs with real examples in every imaginable category of sleight of hand。 I’m much better informed now。 I might even apply some of the techniques to train young scholars to develop a critical mind analyzing reports， statistics， graphs and charts。
Deepak K
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A nice guide， describing the various ways of bullshit appearing as normal fact and how to identify them。 For most parts， the details are quite good with nice and satisfying examples， with the authors also providing a guide to refute such claims。 There were many important learnings and points to remember that I gathered from this book。About numbers:• Graphs may subtly show causality， which may not exist。 There is a big difference between causation and correlation， which tend to get missed。• Data A nice guide， describing the various ways of bullshit appearing as normal fact and how to identify them。 For most parts， the details are quite good with nice and satisfying examples， with the authors also providing a guide to refute such claims。 There were many important learnings and points to remember that I gathered from this book。About numbers:• Graphs may subtly show causality， which may not exist。 There is a big difference between causation and correlation， which tend to get missed。• Data depicted as numbers usually messes with the causes， especially between a Probabilistic cause （A increases the chance of B in a casual manner）， Sufficient cause （if A happens， B always happens） and Necessary cause （unless A happens， B cant happen）。 The distinction between necessary and sufficient causes is sometimes misused， particularly by those interested in denying casual relationships。 For example， Mike Pence once made the following argument against government regulation of tobacco:"Time for a quick reality check。 Despite the hysteria from the political class and media， smoking doesn't kill。 In fact， 2 out of every three smokers does not die from a smoking related illness and 9 out of ten smokers do not contract lung cancer。 "This is just plain bullshit， and bullshit of a higher grade than usually appears in print。 In one sentence Pence says literally "Smoking doesn't kill"， and in the very next he says that a third of smokers die of smoking related illness。 Pence is conflating sufficient cause with probabilistic cause。 Smoking is not sufficient to guarantee lung cancer or even smoking-related illness， but it does greatly increase the probability that someone will die of one or the other。 A related argument might be that smoking doesn't cause lung cancer because some lung cancer victims - miners， for example - never smoked。 This argument conflates necessary cause with probabilistic cause。 • While exploring percentages a common mistake is to ignore Denominators， which skew percentage。 Another is negative values which when clubbed together can show increase （for ex， slight increase in jobs in Wisconsin while job loss in overall US can show job increase in Wisconsin is huge。）• An example of Gaming system - Rats became a big problem in Indo-China and civilians were rewarded for their extermination。 Rat tails had to be given as proof for rat extermination， but then people gamed the system by harvesting rats。 Similarly， US college administration games the system to increase its rankings。 About selection bias:• Observation selection effects is driven by the association between the very presence of the observer and the variable that the observer reports。 For ex， the long wait time for bus experienced。• Right censoring data is the process of removing data from final analysis because it doesnt fit （for ex， in study about musicians death， the still living musicians are removed out thus skewing the average） and this skews the conclusion。 About data Visualization:• Ducks decorate or obscure the meaningful data in a graphic by aiming to be cute。 Glass slippers on the other hand create a false sense of rigor by shoehorning one type of data into a wholly unsuitable data Visualization。• Vertical axis going down to 0 is important in bar diagram but not so in line。 Bar charts designed to tell stories about magnitude， while line chart is for stories about changes。About Big data• Undue stress on Machine learning is not really important， as they can be unreliable because input data is important for such models。• Machine learning can take cue from unrelated data， for ex， in an x-ray used to detect pneumonia， a word in the x- ray 'portable" was used as cue to identify person with pneumoniaAbout Science:• Prosecutors fallacy （probability）• While publishing science results， a p-value of < 0。05 is preferred， this means that a result obtained by chance is only 5%。 p-hacking is done （by cutting/discarding data） to make the experiment result down to 0。05。• The best way to manage science experiments is to define what specific condition （hypothesis） is going to be tested prior to data collection rather than fitting hypothesis to the data collected。• Not publishing negative results also has an impact， as it may result in fast postives being considered successful results。The following tips are provided for spotting bullshit:• Question the source of information• Beware of unfair comparisons• If it seems too good or too bad to be true， it probably is。• Think in order of magnitude。 Use fermi estimation - a quick back of the hand estimation。• Avoid confirmation bias• Consider multiple hypothesisThe following tips are provided for spotting bullshit online:- Corroborate and triangulate- Pay attention to where information comes from- Dig back to the origin of the story- Use reverse image lookup- Be aware of deepfakes and other synthetic media- Take advantage of fact checking organizations- Make sure you kkow who you are dealing with- Consider websites track record- Be aware of illusory truth effects- Reduce your information intakeThe following tips are provided for Refuting bullshit:• Use reductio ad absurdum• Be memorable• Find counterexamples• Provide analogies• Redraw figures• Deploy a null model• The psychology of debunkingOverall， a useful guide in today's world and age。 New things:• Paltering - Deliberately leading one to draw wrong conclusions by saying things that are technically not untrue。• Weasel wording - use the gap between literal meaning and implicature to avoid taking responsibility for things。• Brandolini's principle - The amount of energy needed to refute bullshit is an order of magnitude bigger than [that needed] to produce it。• Goodharts law - When a measure becomes a target， it ceases to be a good measure。• Prosecutor's fallacy - A fallacy of statistical reasoning involving a test for an occurrence， such as a DNA match。 A positive result in the test may paradoxically be more likely to be an erroneous result than an actual occurrence， even if the test is very accurate。 The fallacy is named because it is typically used by a prosecutor to exaggerate the likelihood of a criminal defendant's guilt。 The fallacy can be used to support other claims as well – including the innocence of a defendant。• Andrew Wakefield - published the study that triggered anti-vaccine debates and is still used today。 。。。more
Julie Huskey
，
This may not be as entertaining as the other book about the current infoscape I read recently， Matt Taibbi's Hate， Inc。 -- it lacks the insults and the campaign-trail anecdotes -- but it was hard to put down。 I can imagine it as required reading for first-year college students。 This may not be as entertaining as the other book about the current infoscape I read recently， Matt Taibbi's Hate， Inc。 -- it lacks the insults and the campaign-trail anecdotes -- but it was hard to put down。 I can imagine it as required reading for first-year college students。 。。。more
Iliya
，
The skills to spot and refute lies and misinformation in all of their forms - written， visual （graphs and charts）， numerical （statistics）， etc。 are essential and I think this book， along with "A Field Guide to Lies" by Daniel J Levitin should be taught in high school。 Our society just can't function properly if most of the people make decisions based on false and misleading information， especially when those decisions affect the rest of the society。The book is based on a university course led by The skills to spot and refute lies and misinformation in all of their forms - written， visual （graphs and charts）， numerical （statistics）， etc。 are essential and I think this book， along with "A Field Guide to Lies" by Daniel J Levitin should be taught in high school。 Our society just can't function properly if most of the people make decisions based on false and misleading information， especially when those decisions affect the rest of the society。The book is based on a university course led by two college professors at the University of Washington。 I like how the content is structured and the practical examples。 。。。more
Randy
，
This a good book， and I really like some chapters such as Causality， Data Visualization， Big Data， and Susceptibility of Science。 The examples in those chapters （and some others） are excellent， and I'm glad that the authors did not use some well-known cases。 Still， I like some other books more， although their focuses are a little different:Proofiness by Charles SeifeHow not to be wrong by Jordan EllenbergCoincidences， chaos， and all that math jazz by Edward BurgerThe skeptics' guide to the unive This a good book， and I really like some chapters such as Causality， Data Visualization， Big Data， and Susceptibility of Science。 The examples in those chapters （and some others） are excellent， and I'm glad that the authors did not use some well-known cases。 Still， I like some other books more， although their focuses are a little different:Proofiness by Charles SeifeHow not to be wrong by Jordan EllenbergCoincidences， chaos， and all that math jazz by Edward BurgerThe skeptics' guide to the universe by Steven NovellaForgot to mention one thing: I don't know if I didn't pay close attention， the way to use notes （references） is maddening。 Usually there were 2 ways: by numerical （I prefer） or by content （I dislike）。 For some reason， they used alphabetical order of authors。 This made it really difficult to follow。 。。。more
Kuba
，
It's like 'How to Lie with Statistics' （1982） but with extra chapters， making it longer， but up-to-date and more comprehensive。 It could be a textbook， a basis for a course study on 'Spotting and Fighting Fake News' if we taught this stuff in public schools。 It's like 'How to Lie with Statistics' （1982） but with extra chapters， making it longer， but up-to-date and more comprehensive。 It could be a textbook， a basis for a course study on 'Spotting and Fighting Fake News' if we taught this stuff in public schools。 。。。more
Howard Altschuler
，
"What bothers us about ducks is that the attempt to be cute makes it harder for the reader to understand the underlying data" say the authors in Chapter 7。They didn't follow their own advice。 They acted as if they invented the word "bullshit"。 The word is repeated ad nauseum throughout the book， particularly in the beginning。 It is like a graph with illustrations that cloud the graphs meaning， at best， it districts， at worst， it distorts the data itself。 Though there are interesting topics cover "What bothers us about ducks is that the attempt to be cute makes it harder for the reader to understand the underlying data" say the authors in Chapter 7。They didn't follow their own advice。 They acted as if they invented the word "bullshit"。 The word is repeated ad nauseum throughout the book， particularly in the beginning。 It is like a graph with illustrations that cloud the graphs meaning， at best， it districts， at worst， it distorts the data itself。 Though there are interesting topics covered， I find the repeated use of the common word bullshit be a transparent and lame attempt to add color to a book about cutting through the 。。。。 frozen cheese。。。 in claims made by politicians， scientists， etc etc。 Interesting topic， but the writing style is a turn off。 On the other hand， there is some useful information。 Though I did not like the stylistic approach of the book， the authors do point out ways reported research is not always what it appears to be。 。。。more
Biff
，
Interesting observations on how bullshit works in American society， although it could have done just as well without the massive use of statistical data。 The two authors teach a course with the same name at U of Washington and some of the sessions can be seen on YouTube。 A quote from Jonathan Swift in 1710: "Falsehood flies， and truth comes limping after it" was worth noting since BS typically is exposed after the damage is done。 I learned a new word: "Paltering"。 This means misleading without l Interesting observations on how bullshit works in American society， although it could have done just as well without the massive use of statistical data。 The two authors teach a course with the same name at U of Washington and some of the sessions can be seen on YouTube。 A quote from Jonathan Swift in 1710: "Falsehood flies， and truth comes limping after it" was worth noting since BS typically is exposed after the damage is done。 I learned a new word: "Paltering"。 This means misleading without lying， often used by members of the political class to establish "plausible deniability"。 The authors suggest that spotting BS is a private activity while calling BS is a public one。The bottom line: We must all be skeptics。 。。。more
Rob
，
It’s very rare that I can wholeheartedly recommend a book， but with this I can。 It’s great。 Read it。
A
，
The book explains what is （according to the authors） bullshit， why is it bad， how to spot it and refute it。 In that sense， the book is excellent， showing diverse examples of bullshit in social media， ted talks and science。 Sometimes the examples get a bit boring， but still， I think everybody should read it， especially the last 2-3 chapters。
Morteza Karami
，
Probably too long as usual。 You can skip through extra lines but grasp on the ideas。 Good as a reminder for a professional and a better to be a gift to your friends who keeps sharing BS on social media。 My favorite takeaway？！ “Correlation does not imply causation。”
Zhivko Kabaivanov
，
Calling Bullshit （2020） is a guide to navigating the huge amounts of bullshit that surround us。 By being alert to the ways in which data and scientific processes get manipulated， we can learn to call out bullshit when we see it。
Jung
，
Bullshit is the all-too-common art of persuading people of something， while not really caring about the truth。 In the modern world of social media and big data， you need to be extra wary that you don’t get taken in。 By understanding that correlation does not imply causation， considering what the numbers we’re told really mean in context， and being skeptical about the merits of datasets， you can better equip yourself to tell fact from bullshit。Actionable advice:Call bullshit by getting the facts Bullshit is the all-too-common art of persuading people of something， while not really caring about the truth。 In the modern world of social media and big data， you need to be extra wary that you don’t get taken in。 By understanding that correlation does not imply causation， considering what the numbers we’re told really mean in context， and being skeptical about the merits of datasets， you can better equip yourself to tell fact from bullshit。Actionable advice:Call bullshit by getting the facts right。Simply identifying bullshit isn’t enough。 It’s up to all of us to call out bullshit when we see it， so that more and more people can see how often we’re taken in by bogus statistics。 But when you do this， it’s vital to get the facts right。 So make sure you have the correct figures in hand before you start taking someone else to task。 And if you make a mistake， admit it。 Otherwise， you’re just another bulshitter。 。。。more
QUINNS
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Bullshit is the all-too-common art of persuading people of something while not caring about the truth。 In the age of social media and big data， we need to be extra wary of not getting taken in。 One could better equip themselves to tell fact from bullshit。 It is these: By understanding that correlation does not imply causation， considering what the numbers mean in context， and being sceptical about the merits of datasets。 Finally， simply identifying bullshit isn't enough。 The authors advise you t Bullshit is the all-too-common art of persuading people of something while not caring about the truth。 In the age of social media and big data， we need to be extra wary of not getting taken in。 One could better equip themselves to tell fact from bullshit。 It is these: By understanding that correlation does not imply causation， considering what the numbers mean in context， and being sceptical about the merits of datasets。 Finally， simply identifying bullshit isn't enough。 The authors advise you to get the facts right before you start taking someone else to the task。 It would be best if you also admitted any mistakes in the process。 You're another bulshitter otherwise。 。。。more
Vít Baisa
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Funny and important book。
Sharon
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Excellent book that every teenager and conspiracy theorist should read。。。 and probably everyone on my Facebook feed too。
Amirmansour Khanmohammad
，
A fascinating read， great ideas from causality to statistics， from ethics to science。 After reading books of Neil Postman， I always regretted that he couldn’t see the internet and social media era to adjust his brilliant books accordingly， but with reading this book， I think the gap is filled。
Emre Ergin
，
Definitely longer than it should be。 I think it succeeds at what it was set to do， but with twice the number of pages that was necessary。 Too many personal anecdotes and too much preaching。To be honest， I am not sure whether I will be more impressed if I was not already an economist， though。As usual， here is the summary （in Turkish）: https://emrergin。github。io/zettel/cal。。。 Definitely longer than it should be。 I think it succeeds at what it was set to do， but with twice the number of pages that was necessary。 Too many personal anecdotes and too much preaching。To be honest， I am not sure whether I will be more impressed if I was not already an economist， though。As usual， here is the summary （in Turkish）: https://emrergin。github。io/zettel/cal。。。 。。。more
Mike Utt
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This book is interesting， but a bit uneven - some chapters better than others。 The authors are not the best authorities on presentation graphics， for example。 There are some good tips in here on when to be suspicious of research results， and the need to avoid confirmation bias。
Laurentiu
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A delightful backpack of mental instruments necessary， if not essential， for any individual that seeks clarity and cohesion in public discourse。 As well as representing a literal call-out to debunking myths， lies and statistics in a polite and argumentative way。 From avoiding self confirmation biases to spotting false information all the way to educating yourself and others on how to read beyond attractive headlines and impressive figures in academia and media。
Brandon Van
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The authors do a great job of encouraging critical thinking in the face of claims that appeal to numbers for credibility。 Calling Bullshit is packed with enough examples， light-hearted writing， and humility to help convey the book’s ideas。 However， some in-text citations for their arguments would have been useful in helping readers heed the advice of checking source material。
Joel Bastos
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In a world where bullshit seems to proliferate， this book aims to help understand its nature， better detect it， and refute it。As stated by Brandolini's law， "The amount of energy needed to refute bullshit is an order of magnitude larger than to produce it"。 This bullshit asymmetry makes it trivial to understand why bullshit endures and grows over time。In my opinion， the contents of this book could neatly integrate into school education and better prepare younger generations to deal with all the In a world where bullshit seems to proliferate， this book aims to help understand its nature， better detect it， and refute it。As stated by Brandolini's law， "The amount of energy needed to refute bullshit is an order of magnitude larger than to produce it"。 This bullshit asymmetry makes it trivial to understand why bullshit endures and grows over time。In my opinion， the contents of this book could neatly integrate into school education and better prepare younger generations to deal with all the bullshit the world has to offer。Some of the most memorable quotes:"Jargon may facilitate technical communication within a field， but it also serves to exclude those who have not been initiated into the inner circle of a discipline。""adequate bullshit detection is essential for the survival of liberal democracy。""Machines are not free of human biases; they perpetuate them， depending on the data they're fed。" 。。。more
Will Ansbacher
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A very useful little book that provides techniques for detecting and calling out both bullshit and lies， with a particular focus on quantitative science。 The authors （who teach a course based on this material） observe that one significant issue with science is the specialized language and insider techniques that make it impenetrable to the outsider， something that doesn’t apply so much in other fields such as advertising or politics。 And precisely because of that barrier， “science-y” language ha A very useful little book that provides techniques for detecting and calling out both bullshit and lies， with a particular focus on quantitative science。 The authors （who teach a course based on this material） observe that one significant issue with science is the specialized language and insider techniques that make it impenetrable to the outsider， something that doesn’t apply so much in other fields such as advertising or politics。 And precisely because of that barrier， “science-y” language has been co-opted by other disciplines intent on bullshitting。But the authors emphasize that it often isn’t necessary to have detailed knowledge of any discipline in order to see through lies and disinformation。 They point out that essentially all research involves some kind of “black box” containing procedures （such as advanced statistical methods or analytical techniques） that are accessible only to experts in the field and whose details are mostly unknowable to the non-specialist， but in the end can be reduced to:Data -> Black Box -> OutputAnd indeed， for systems like AI or Machine Learning， they note that even the practitioners don’t know what’s inside the box。 Thus， it’s often sufficient just to identify inconsistencies in the input or output， which would invalidate anything that happens in the black box。So “Calling Bullshit” involves critically examining the input assumptions， and the authors provide many examples and strategies for doing that。 Yes， you do need a certain level of numeracy here – knowledge of averages， order of magnitude estimates， cause and effect and the like - but it isn’t necessary， for example， to have a detailed understanding of statistics to discover that Selection Bias exists in a set of assumptions。 That chapter on selection bias was one of the most entertaining – it explains why your friends really do have more friends than you do （on average， of course！） The authors do distinguish outright lying – where the liar goes to some length to make their lie believable - from bullshitting， where the shitter doesn’t even care whether you believe them or not， but that isn’t the main point of the book。 A particularly good example is Wakefield’s dangerous and fallacious vaccine-autism link。The final chapter is about how to call （refute） bullshit and to get bullshitters to stop; I’m less convinced here， though。 Be correct， be charitable， admit your own faults， be clear， be pertinent – yes， these are all necessary， rational responses， but may not be sufficient。 You do have to choose your battles。 。。。more
Fernando Gordillo
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